Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements

Rafhael Medeiros de Amorim; Jeroen Wigard; Huan Cong Nguyen; Istvan Kovacs; Preben Elgaard Mogensen

doi:10.1109/GLOCOMW.2017.8269067

Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements

Rafhael Medeiros de Amorim, Jeroen Wigard, Huan Cong Nguyen, Istvan Kovacs, Preben Elgaard Mogensen

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

15 Citations (Scopus)

Abstract

The overall cellular network performance can be optimized for both ground and aerial users, if different treatment is given for the two user classes. Airborne UAVs experience
different radio conditions that terrestrial users due to clearance in the radio path, which leads to strong desired signal reception, but at the same time increases the interference. Based on this, one can for instance use different interference coordination techniques for aerial users as for terrestrial user and/or use specific mobility settings for each class. This paper compares three different classification algorithms, which use standard LTE measurements from the UE as input, for detecting the presence of airborne users in the network. The algorithms are evaluated based on measurements done with mobile phones attached under a flying drone and on a car. Results are discussed showing the advantages and drawbacks for each option regarding different use cases, and the compromise between specificity and sensibility. For the collected data results show reliability close to 99% in most cases and also discuss how waiting for the final decision can even improve this accuracy to values close to 100%.

Original language	English
Title of host publication	Globecom Workshops (GC Wkshps), 2017 IEEE
Number of pages	6
Publisher	IEEE
Publication date	Dec 2017
ISBN (Electronic)	978-1-5386-3920-7
DOIs	https://doi.org/10.1109/GLOCOMW.2017.8269067
Publication status	Published - Dec 2017
Event	IEEE GLOBECOM 2017: Global Hub: Connecting East and West - , Singapore Duration: 4 Dec 2017 → 8 Dec 2017 http://globecom2017.ieee-globecom.org/

Conference

Conference	IEEE GLOBECOM 2017
Country/Territory	Singapore
Period	04/12/2017 → 08/12/2017
Internet address	http://globecom2017.ieee-globecom.org/

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10.1109/GLOCOMW.2017.8269067

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@inproceedings{c13a2269b6744bfdaa975254c42ebfb3,

title = "Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements",

abstract = "The overall cellular network performance can be optimized for both ground and aerial users, if different treatment is given for the two user classes. Airborne UAVs experiencedifferent radio conditions that terrestrial users due to clearance in the radio path, which leads to strong desired signal reception, but at the same time increases the interference. Based on this, one can for instance use different interference coordination techniques for aerial users as for terrestrial user and/or use specific mobility settings for each class. This paper compares three different classification algorithms, which use standard LTE measurements from the UE as input, for detecting the presence of airborne users in the network. The algorithms are evaluated based on measurements done with mobile phones attached under a flying drone and on a car. Results are discussed showing the advantages and drawbacks for each option regarding different use cases, and the compromise between specificity and sensibility. For the collected data results show reliability close to 99% in most cases and also discuss how waiting for the final decision can even improve this accuracy to values close to 100%.",

author = "Amorim, {Rafhael Medeiros de} and Jeroen Wigard and Nguyen, {Huan Cong} and Istvan Kovacs and Mogensen, {Preben Elgaard}",

year = "2017",

month = dec,

doi = "10.1109/GLOCOMW.2017.8269067",

language = "English",

booktitle = "Globecom Workshops (GC Wkshps), 2017 IEEE",

publisher = "IEEE",

address = "United States",

note = "IEEE GLOBECOM 2017 : Global Hub: Connecting East and West, Globecom '17 ; Conference date: 04-12-2017 Through 08-12-2017",

url = "http://globecom2017.ieee-globecom.org/",

}

TY - GEN

T1 - Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements

AU - Amorim, Rafhael Medeiros de

AU - Wigard, Jeroen

AU - Nguyen, Huan Cong

AU - Kovacs, Istvan

AU - Mogensen, Preben Elgaard

PY - 2017/12

Y1 - 2017/12

N2 - The overall cellular network performance can be optimized for both ground and aerial users, if different treatment is given for the two user classes. Airborne UAVs experiencedifferent radio conditions that terrestrial users due to clearance in the radio path, which leads to strong desired signal reception, but at the same time increases the interference. Based on this, one can for instance use different interference coordination techniques for aerial users as for terrestrial user and/or use specific mobility settings for each class. This paper compares three different classification algorithms, which use standard LTE measurements from the UE as input, for detecting the presence of airborne users in the network. The algorithms are evaluated based on measurements done with mobile phones attached under a flying drone and on a car. Results are discussed showing the advantages and drawbacks for each option regarding different use cases, and the compromise between specificity and sensibility. For the collected data results show reliability close to 99% in most cases and also discuss how waiting for the final decision can even improve this accuracy to values close to 100%.

AB - The overall cellular network performance can be optimized for both ground and aerial users, if different treatment is given for the two user classes. Airborne UAVs experiencedifferent radio conditions that terrestrial users due to clearance in the radio path, which leads to strong desired signal reception, but at the same time increases the interference. Based on this, one can for instance use different interference coordination techniques for aerial users as for terrestrial user and/or use specific mobility settings for each class. This paper compares three different classification algorithms, which use standard LTE measurements from the UE as input, for detecting the presence of airborne users in the network. The algorithms are evaluated based on measurements done with mobile phones attached under a flying drone and on a car. Results are discussed showing the advantages and drawbacks for each option regarding different use cases, and the compromise between specificity and sensibility. For the collected data results show reliability close to 99% in most cases and also discuss how waiting for the final decision can even improve this accuracy to values close to 100%.

UR - http://globecom2017.ieee-globecom.org/workshop/ws-03-8th-international-workshop-wireless-networking-and-control-unmanned-autonomous-0

U2 - 10.1109/GLOCOMW.2017.8269067

DO - 10.1109/GLOCOMW.2017.8269067

M3 - Article in proceeding

BT - Globecom Workshops (GC Wkshps), 2017 IEEE

PB - IEEE

T2 - IEEE GLOBECOM 2017

Y2 - 4 December 2017 through 8 December 2017

ER -

Machine-Learning Identification of Airborne UAV-UEs Based on LTE Radio Measurements

Abstract

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Performance Enhancements for LTE-Connected UAVs: Experiments and Simulations

How to ensure reliable connectivity for aerial vehicles over cellular networks

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